# audit-labs/tutorials

Learn how to perform data analysis, scripting, automation, and more using reproducible Jupyter Notebooks.

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/audit-labs/tutorials/HEAD)
[![Notebooks](https://img.shields.io/badge/notebooks-Jupyter-orange.svg)]()
[![License](https://img.shields.io/badge/license-GPLv3-blue.svg)]()

Table of contents
- [Project overview](#project-overview)
- [Who this is for](#who-this-is-for)
- [What's included](#whats-included)
- [Getting started](#getting-started)
  - [Prerequisites](#prerequisites)
  - [Quick start — using Binder (no local setup)](#quick-start---using-binder-no-local-setup)
  - [Run locally (recommended)](#run-locally-recommended)
  - [Run in Google Colab](#run-in-google-colab)
  - [Run with Docker](#run-with-docker)
  - [Run headless / export notebooks](#run-headless--export-notebooks)
- [Best practices for notebooks](#best-practices-for-notebooks)
- [Contributing](#contributing)
- [License & Code of Conduct](#license--code-of-conduct)
- [Contact / Support](#contact--support)

## Project overview
This repository contains interactive tutorials and example notebooks designed to teach practical skills in data analysis, scripting, automation, and related topics using Jupyter Notebooks. Each notebook demonstrates concepts through hands-on examples so you can follow along and adapt the patterns to your own projects.

## Who this is for
- Data analysts and engineers learning reproducible workflows.
- Developers who want to prototype automation or analysis in notebooks.
- Students and instructors seeking ready-made examples for teaching.

## What's included
- A collection of Jupyter Notebook tutorials (look in the repository root or `notebooks/` folder for .ipynb files).
- Guidance and examples that demonstrate common patterns for data ingestion, transformation, visualization, and basic automation.

(If your repo has a specific folder layout or important notebooks, consider adding a short list here with links to the most important notebooks.)

## Getting started

### Prerequisites
- Python 3.10+ (3.10 recommended)
- Git (to clone the repo)
- JupyterLab or Jupyter Notebook (for local development)

Optional:
- Conda (recommended for reproducible environments)
- Docker (for containerized runs)

This repository is licensed under the GNU General Public License v3.0 (GPL-3.0). See the included LICENSE file for details.

### Quick start — using Binder (no local setup)
To run the notebooks in your browser with no local install, use Binder:

- Launch Binder: https://mybinder.org/v2/gh/audit-labs/tutorials/HEAD

Binder will respect `environment.yml` or `requirements.txt` if present; this repository includes an `environment.yml` to produce a reproducible environment.

### Run locally (recommended)

1. Clone the repository
   ```bash
   git clone https://github.com/audit-labs/tutorials.git
   cd tutorials
   ```

2. Create and activate an environment

   Using conda (recommended):
   ```bash
   conda env create -f environment.yml
   conda activate audit-tutorials
   ```

   Or with pip and virtualenv:
   ```bash
   python -m venv venv
   source venv/bin/activate   # macOS / Linux
   venv\Scripts\activate      # Windows
   pip install --upgrade pip
   pip install -r requirements.txt
   ```

3. Install JupyterLab (if not already)
   ```bash
   pip install jupyterlab
   jupyter lab
   ```
   Or run the classic notebook server:
   ```bash
   jupyter notebook
   ```

4. Open the notebooks in the browser and follow the cells.

### Run in Google Colab
To open a notebook in Colab:
- Navigate to the notebook file on GitHub, then use "Open in Colab" or open via:
  https://colab.research.google.com/github/audit-labs/tutorials/blob/HEAD/path/to/notebook.ipynb
- Colab will run in the cloud; you may need to pip-install extra dependencies at the top of the notebook using `!pip install ...`.

### Run with Docker
You can run the notebooks inside a Docker container using Jupyter's base images:
```
docker run -p 8888:8888 -v "$(pwd)":/home/jovyan/work jupyter/base-notebook:latest
```
Then open `http://localhost:8888` and navigate to `work/`.

### Run headless / export notebooks
To execute notebooks and export them programmatically:
```
pip install nbconvert nbclient
jupyter nbconvert --to html --execute path/to/notebook.ipynb
```
This is useful for CI pipelines and automated report generation.

## Best practices for notebooks
- Keep notebooks focused: one concept or analysis per notebook.
- Include a short README or top-level markdown cell describing purpose and inputs.
- Avoid long-running data downloads inside notebooks—prefer referencing local sample data or scripts.
- Use version control: commit notebooks regularly. Consider tools like `nbstripout` or `nbdime` to manage diffs.
- Parametrize notebooks for reproducibility (e.g., use papermill for parameterized runs).

## Contributing
See CONTRIBUTING.md for contribution guidelines.

## License & Code of Conduct
This project is licensed under the GNU General Public License v3.0 (GPL-3.0). See [LICENSE](./LICENSE) for details.

Please review CODEOFCONDUCT.md for expected behavior when contributing.

## Contact / Support
For questions or help, open an issue in this repository or contact the maintainers listed in the repository settings.

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